Unmet medical need exists for serious bacterial diseases caused by multidrug-resistant infections, necessitating an urgent need for newer therapies with greater treatment benefits to patients. For meeting this need, the usual approach has been to conduct separate clinical trials, each trial targeting infection at a single body-site, e.g., for respiratory tract, intra-abdominal site, urinary tract, or blood. However, for the unmet medical need situations, this approach seems inefficient for developing antibacterial drugs with activity against single species or against multiple species of bacteria for a broader indication. Instead, a streamlined clinical development program for such situations can benefit by considering multiple body-site infection trials. Such trials would enroll patients with infections at different body-sites, but with similar severity and comorbidity for avoiding potential treatment effect heterogeneity. Such trials, when properly designed and conducted, can be informative and can save time and resources in drug development. Goals for such trials would be to first demonstrate that there is evidence of an overall treatment effect, and then to show that the treatment effects at individual body-sites reveal consistency in contributing to the overall treatment effect, or to identify a subset of body-sites for which greater treatment effect can be supported by a specified statistical decision criterion. For this, we propose here an information-based procedure for the demonstration of treatment effect overall across all body-sites, or for a subset of body-sites, on considering two types of error rates of falsely concluding treatment effect.

To evaluate the analytical similarity between the proposed biosimilar product and the US-licensed reference product, a working group at Food and Drug Administration (FDA) developed a tiered approach. This proposed tiered approach starts with a criticality determination of quality attributes (QAs) based on risk ranking of their potential impact on product quality and the clinical outcomes. Those QAs characterize biological products in terms of structural, physico-chemical, and functional properties. Correspondingly, we propose three tiers of statistical approaches based on its levels of stringent in requirements. The three tiers of statistical approaches will be applied to QAs based on the criticality ranking and other factors. In this article, we discuss the statistical methods applicable to the three tiers of QA. We further provide more details for the proposed equivalence test as the tier 1 approach. We also provide some discussion on the statistical challenges of the proposed equivalence test in the context of analytical similarity assessment.

To evaluate the analytical similarity between the proposed biosimilar product and the US-licensed reference product, U.S. Food and Drug Administration (FDA) statisticians collaborated with Chemistry, Manufacturing and Control (CMC) scientists at FDA in order to develop a three-tier approach. The proposed tiered approach starts with a criticality determination of quality attributes (QAs) based on their potential impact on product quality and the clinical outcomes. Those QAs characterize the biological product in terms of structural, physico-chemical, and functional properties. Then, the QAs are assigned into three tiers based on their criticality ranking. To evaluate the analytical similarity for QAs assigned to different tiers, we recommend different statistical approaches with different statistical rigors. That is, we recommend an equivalence test for the critical quality attributes (CQAs) in Tier 1, a quality range approach for QAs in Tier 2, and a side-by-side graphic comparison approach for QAs in Tier 3. In this article, we mainly focus on the development of the FDAs recommended equivalence test for Tier 1. We also provide some discussion on the statistical challenges of the proposed equivalence test in the context of analytical similarity assessment.